Forecasting Energy Consumption with XGBoost

Python

Pandas

Seaborn

XGBoost

Informed by YouTube videos of Rob Mulla we use XGBoost to forecast energy consumption in the eastern US.

Author

Daniel J Smith

Published

December 22, 2023

import numpy as np 
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
color_pal = sns.color_palette('magma')

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import TimeSeriesSplit

import xgboost as xgb

print("Python version:")
!python --version

Python version:
Python 3.11.4

Data

The data can be downloaded here. According to the Kaggle page:

PJM Interconnection LLC (PJM) is a regional transmission organization (RTO) in the United States. It is part of the Eastern Interconnection grid operating an electric transmission system serving all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia. The hourly power consumption data comes from PJM’s website and are in megawatts (MW).

df = pd.read_csv('PJME_hourly.csv')

df.head()

	Datetime	PJME_MW
0	2002-12-31 01:00:00	26498.0
1	2002-12-31 02:00:00	25147.0
2	2002-12-31 03:00:00	24574.0
3	2002-12-31 04:00:00	24393.0
4	2002-12-31 05:00:00	24860.0

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 145366 entries, 0 to 145365
Data columns (total 2 columns):
 #   Column    Non-Null Count   Dtype  
---  ------    --------------   -----  
 0   Datetime  145366 non-null  object 
 1   PJME_MW   145366 non-null  float64
dtypes: float64(1), object(1)
memory usage: 2.2+ MB

df.describe()

	PJME_MW
count	145366.000000
mean	32080.222831
std	6464.012166
min	14544.000000
25%	27573.000000
50%	31421.000000
75%	35650.000000
max	62009.000000

Use the ‘Datetime’ column as the dataframe index, and convert to the Datetime type

df = df.set_index('Datetime')
df.index = pd.to_datetime(df.index)

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 145366 entries, 2002-12-31 01:00:00 to 2018-01-02 00:00:00
Data columns (total 1 columns):
 #   Column   Non-Null Count   Dtype  
---  ------   --------------   -----  
 0   PJME_MW  145366 non-null  float64
dtypes: float64(1)
memory usage: 2.2 MB

df.head()

	PJME_MW
Datetime
2002-12-31 01:00:00	26498.0
2002-12-31 02:00:00	25147.0
2002-12-31 03:00:00	24574.0
2002-12-31 04:00:00	24393.0
2002-12-31 05:00:00	24860.0

df.plot(style='.',
        figsize=(15, 5),
        color=color_pal[0],
        title='PJME Energy Use in MW')
plt.ylabel('Energy Consumption (MW)')

Text(0, 0.5, 'Energy Consumption (MW)')

Periodic Trends

week = df.loc[(df.index >'01-01-2010') & (df.index < '01-08-2010')]

fig, ax = plt.subplots(figsize=(10, 6))
sns.lineplot(data=week, ax=ax)
plt.title('One Week of Data (01-01-2010 to 01-08-2010)', fontsize=18)
plt.ylabel('Energy Consumption (MW)', fontsize=14)

plt.xticks(rotation=45)
plt.grid(True)

selected_date = '01-01-2010'
selected_day = df.loc[selected_date]

fig, ax = plt.subplots(figsize=(10, 6))
sns.lineplot(data=selected_day, ax=ax)
plt.title(f'One Day of Data ({selected_date})', fontsize=18)

plt.xlabel('Time', fontsize=14)
plt.ylabel('Energy Consumption (MW)', fontsize=14)
plt.xticks(rotation=45)
plt.grid(True)

One might worry that new year’s day might not be a typical day for energy consumption.

Repeating this plot for the first of February:

selected_date = '01-02-2010'
selected_day = df.loc[selected_date]

fig, ax = plt.subplots(figsize=(10, 6))
sns.lineplot(data=selected_day, ax=ax)

plt.title(f'One Day of Data ({selected_date})', fontsize=18)
plt.xlabel('Time', fontsize=14)
plt.ylabel('Energy Consumption (MW)', fontsize=14)
plt.xticks(rotation=45)
plt.grid(True)

It seems that the daily trend seems to be the same.

To be sure, we repeat this plot once more for four randomly chosen dates in 2005-2010:

start_date = '01-01-2005'
end_date = '12-31-2010'

date_range = pd.date_range(start=start_date, end=end_date)

fig, axs = plt.subplots(2, 2, figsize=(12, 10), gridspec_kw={'hspace': 0.5})
axs = axs.flatten()
colors = ['#FFB6C1', '#87CEFA', '#98FB98', '#FFD700']

for i in range(4):
    # Choose a random day
    random_date = pd.to_datetime(np.random.choice(date_range))
    day_data = df[df.index.date == random_date.date()]

    # Create a subplot
    ax = axs[i]

    sns.lineplot(data=day_data, ax=ax, palette=[colors[i]])

    ax.set_title(f'{random_date.date()}', fontsize=14)
    ax.set_xlabel('Time', fontsize=12)
    ax.set_ylabel('Energy Consumption (MW)', fontsize=12)

    ax.tick_params(axis='x', rotation=45)
    ax.grid(True)

Unsuprisingly, the time of year seems to have a significant impact on daily energy consumption patterns.

The very general trend of less energy consumption at night compared with in the day seems however to persist across the year.

Creating another 2x2 grid, this time plotting the energy consumption across random weeks in 2005-2010:

start_date = '01-01-2005'
end_date = '12-31-2010'

date_range = pd.date_range(start=start_date, end=end_date)

fig, axs = plt.subplots(2, 2, figsize=(12, 10), gridspec_kw={'hspace': 0.5})
axs = axs.flatten()
colors = ['#FFB6C1', '#87CEFA', '#98FB98', '#FFD700']

for i in range(4):
    # Choose a random week
    random_start_date = pd.to_datetime(np.random.choice(date_range))
    start_date = random_start_date
    end_date = start_date + pd.DateOffset(days=7)
    week_data = df.loc[(df.index >= start_date) & (df.index < end_date)]
    
    # Create a subplot
    ax = axs[i]

    sns.lineplot(data=week_data, ax=ax, palette=[colors[i]])
    
    week_year_string = start_date.strftime('Week %U, %Y')
    
    ax.set_title(f'{week_year_string}', fontsize=14)
    ax.set_xlabel('Time', fontsize=12)
    ax.set_ylabel('Energy Consumption (MW)', fontsize=12)

    ax.tick_params(axis='x', rotation=45)
    ax.grid(True)
    
plt.savefig('header.png')

Outlier Analysis and Removal

df['PJME_MW'].plot(kind='hist',
                   bins=500,
                   color=color_pal[1])
plt.xlabel('Energy Consumption (MW)')

Text(0.5, 0, 'Energy Consumption (MW)')

df.query('PJME_MW < 20_000').plot(figsize=(15,5),
                                  style='x',
                                  color='r')

<Axes: xlabel='Datetime'>

The data points sitting signficantly below 20,000 in 2013 seem to be highly suspect.

Lower the query threshold to 19,00 in an attempt to isolate these points:

df.query('PJME_MW < 19_000').plot(figsize=(15,5),
                                  style='x',
                                  color='r')

<Axes: xlabel='Datetime'>

We’ve successfully isolated these outliers, now filtering them out:

df = df.query('PJME_MW >= 19_000').copy()

len(df.query('PJME_MW < 19_000'))

Feature Creation

def create_features(df):
    '''
    Create time series features using pd DateTime object methods on dataframe index
    '''
    df = df.copy()
    df['hour'] = df.index.hour
    df['dayofweek'] = df.index.dayofweek
    df['quarter'] = df.index.quarter
    df['month'] = df.index.month
    df['year'] = df.index.year
    df['dayofyear'] = df.index.dayofyear
    return df

df = create_features(df)

df

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear
Datetime
2002-12-31 01:00:00	26498.0	1	1	4	12	2002	365
2002-12-31 02:00:00	25147.0	2	1	4	12	2002	365
2002-12-31 03:00:00	24574.0	3	1	4	12	2002	365
2002-12-31 04:00:00	24393.0	4	1	4	12	2002	365
2002-12-31 05:00:00	24860.0	5	1	4	12	2002	365
...	...	...	...	...	...	...	...
2018-01-01 20:00:00	44284.0	20	0	1	1	2018	1
2018-01-01 21:00:00	43751.0	21	0	1	1	2018	1
2018-01-01 22:00:00	42402.0	22	0	1	1	2018	1
2018-01-01 23:00:00	40164.0	23	0	1	1	2018	1
2018-01-02 00:00:00	38608.0	0	1	1	1	2018	2

145351 rows × 7 columns

Lag Features

def add_lags(df):
    target_map = df['PJME_MW'].to_dict()
    df['lag1'] = (df.index - pd.Timedelta('364 days')).map(target_map)
    df['lag2'] = (df.index - pd.Timedelta('728 days')).map(target_map)
    df['lag3'] = (df.index - pd.Timedelta('1092 days')).map(target_map)
    return df

df = add_lags(df)

df

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear	lag1	lag2	lag3
Datetime
2002-12-31 01:00:00	26498.0	1	1	4	12	2002	365	30393.0	NaN	NaN
2002-12-31 02:00:00	25147.0	2	1	4	12	2002	365	29265.0	NaN	NaN
2002-12-31 03:00:00	24574.0	3	1	4	12	2002	365	28357.0	NaN	NaN
2002-12-31 04:00:00	24393.0	4	1	4	12	2002	365	27899.0	NaN	NaN
2002-12-31 05:00:00	24860.0	5	1	4	12	2002	365	28057.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
2018-01-01 20:00:00	44284.0	20	0	1	1	2018	1	34600.0	41772.0	40740.0
2018-01-01 21:00:00	43751.0	21	0	1	1	2018	1	33674.0	41393.0	40049.0
2018-01-01 22:00:00	42402.0	22	0	1	1	2018	1	32035.0	40092.0	38473.0
2018-01-01 23:00:00	40164.0	23	0	1	1	2018	1	29870.0	37911.0	35962.0
2018-01-02 00:00:00	38608.0	0	1	1	1	2018	2	27771.0	35931.0	33680.0

145351 rows × 10 columns

Visualising Feature/Target Relationships

fig, ax = plt.subplots(figsize=(10,8))
sns.boxplot(data=df, x='hour', y='PJME_MW', palette='magma')
ax.set_title('Energy Consumption (MW) by Hour')
plt.grid(True)

fig, ax = plt.subplots(figsize=(10,8))
sns.boxplot(data=df, x='month', y='PJME_MW', palette='coolwarm')
ax.set_title('Energy Consumption (MW) by Month')
plt.grid(True)

Train/Test Split

We reserve the data from 2015 and beyond as a test set, and use the data before 2015 as a training set. The following code produces an illustration to help explain this.

train = df.loc[df.index < '01-01-2015']['PJME_MW']
test = df.loc[df.index >= '01-01-2015']['PJME_MW']

fig, ax = plt.subplots(figsize=(15, 5))

train.plot(ax=ax, 
           label='Training Set',
           title='Data Train/Test Split',
           color=color_pal[3])

test.plot(ax=ax,
          label='Test Set',
          color=color_pal[0])

ax.axvline('01-01-2015', color='black', ls='--')
ax.legend(['Training Set', 'Test Set'])
plt.ylabel('Energy Consumption (MW)')

Text(0, 0.5, 'Energy Consumption (MW)')

Aside - Time Series Cross Validation

To implement cross-validation while training a model on time series data, one can use TimeSeriesSplit from sklearn.

tss = TimeSeriesSplit(n_splits=5, test_size=24*365*1, gap=24)
df = df.sort_index()

The following code produces a visualisation to illustrate how the data is structured into a training set and a test set during 5-fold time series cross-validation.

fig, axs = plt.subplots(5, 1, figsize=(15, 15), sharex=True)

fold = 0
for train_idx, val_idx in tss.split(df):
    train = df.iloc[train_idx]
    test = df.iloc[val_idx]
    train['PJME_MW'].plot(ax=axs[fold],
                          label='Training Set',
                          title=f'Data Train/Test Split Fold {fold}',
                          color=color_pal[3])
    test['PJME_MW'].plot(ax=axs[fold],
                         label='Test Set',
                         color=color_pal[0])
    axs[fold].axvline(test.index.min(), color='black', ls='--')
    fold += 1

Training using Cross-Validation

The following code trains an XGBRegressor on each of the above 5 folds, saving the score (RMSE) in a list scores.

fold = 0
preds = []
scores = []
for train_idx, val_idx in tss.split(df):
    train = df.iloc[train_idx]
    test = df.iloc[val_idx]

    train = create_features(train)
    test = create_features(test)

    FEATURES = ['dayofyear', 'hour', 'dayofweek', 'quarter', 'month','year', 'lag1', 'lag2', 'lag3']
    TARGET = 'PJME_MW'

    X_train = train[FEATURES]
    y_train = train[TARGET]

    X_test = test[FEATURES]
    y_test = test[TARGET]

    reg = xgb.XGBRegressor(base_score=0.5, 
                           booster='gbtree',    
                           n_estimators=1000,
                           early_stopping_rounds=50,
                           objective='reg:squarederror',
                           max_depth=3,
                           learning_rate=0.01)
    
    reg.fit(X_train, 
            y_train,
            eval_set=[(X_train, y_train), (X_test, y_test)],
            verbose=100)

    y_pred = reg.predict(X_test)
    preds.append(y_pred)
    score = np.sqrt(mean_squared_error(y_test, y_pred))
    scores.append(score)

[0] validation_0-rmse:32732.50147   validation_1-rmse:31956.66494
[100]   validation_0-rmse:12532.10915   validation_1-rmse:11906.70125
[200]   validation_0-rmse:5739.78666    validation_1-rmse:5352.86754
[300]   validation_0-rmse:3868.29390    validation_1-rmse:3891.32148
[400]   validation_0-rmse:3428.85875    validation_1-rmse:3753.95996
[456]   validation_0-rmse:3349.18480    validation_1-rmse:3761.64093
[0] validation_0-rmse:32672.16154   validation_1-rmse:32138.88680
[100]   validation_0-rmse:12513.25338   validation_1-rmse:12222.97626
[200]   validation_0-rmse:5755.14393    validation_1-rmse:5649.54800
[300]   validation_0-rmse:3909.18294    validation_1-rmse:3930.98277
[400]   validation_0-rmse:3477.91771    validation_1-rmse:3603.77859
[500]   validation_0-rmse:3356.63775    validation_1-rmse:3534.18452
[600]   validation_0-rmse:3299.24378    validation_1-rmse:3495.69013
[700]   validation_0-rmse:3258.86466    validation_1-rmse:3470.24780
[800]   validation_0-rmse:3222.68998    validation_1-rmse:3446.36557
[900]   validation_0-rmse:3195.04645    validation_1-rmse:3438.00845
[999]   validation_0-rmse:3169.68251    validation_1-rmse:3434.35289
[0] validation_0-rmse:32631.19070   validation_1-rmse:31073.24659
[100]   validation_0-rmse:12498.56469   validation_1-rmse:11133.47932
[200]   validation_0-rmse:5749.48268    validation_1-rmse:4812.56835
[300]   validation_0-rmse:3915.69493    validation_1-rmse:3552.97165
[400]   validation_0-rmse:3493.17887    validation_1-rmse:3492.55244
[415]   validation_0-rmse:3467.76622    validation_1-rmse:3500.17489
[0] validation_0-rmse:32528.44140   validation_1-rmse:31475.37803
[100]   validation_0-rmse:12461.95683   validation_1-rmse:12016.24890
[200]   validation_0-rmse:5736.08201    validation_1-rmse:5800.02075
[300]   validation_0-rmse:3913.36576    validation_1-rmse:4388.02984
[400]   validation_0-rmse:3495.35688    validation_1-rmse:4177.05330
[500]   validation_0-rmse:3380.70922    validation_1-rmse:4123.43863
[600]   validation_0-rmse:3321.42955    validation_1-rmse:4110.84393
[700]   validation_0-rmse:3280.93068    validation_1-rmse:4096.40531
[800]   validation_0-rmse:3249.14336    validation_1-rmse:4095.30547
[809]   validation_0-rmse:3246.14826    validation_1-rmse:4094.38398
[0] validation_0-rmse:32462.05402   validation_1-rmse:31463.86930
[100]   validation_0-rmse:12445.22753   validation_1-rmse:11954.79556
[200]   validation_0-rmse:5750.85887    validation_1-rmse:5616.16472
[300]   validation_0-rmse:3949.92308    validation_1-rmse:4154.55799
[400]   validation_0-rmse:3538.33857    validation_1-rmse:3996.70155
[448]   validation_0-rmse:3471.50174    validation_1-rmse:4005.60241

scores

[3753.2775219986684,
 3434.3528874818867,
 3475.9138463312997,
 4093.3608331481823,
 3996.298054855067]

print(f'Mean score across folds: {np.mean(scores):0.4f}')
print(f'Fold scores:\n{scores}')

Mean score across folds: 3750.6406
Fold scores:
[3753.2775219986684, 3434.3528874818867, 3475.9138463312997, 4093.3608331481823, 3996.298054855067]

Predicting on Test Set

Fitting an `XGBRegressor`

model = xgb.XGBRegressor(base_score=0.5, 
                         booster='gbtree',    
                         n_estimators=1000,
                         early_stopping_rounds=50,
                         objective='reg:squarederror',
                         max_depth=3,
                         learning_rate=0.01)

model.fit(X_train, 
          y_train, 
          eval_set=[(X_train,y_train),(X_test,y_test)],
          verbose=100)

[0] validation_0-rmse:32462.05402   validation_1-rmse:31463.86930
[100]   validation_0-rmse:12445.22753   validation_1-rmse:11954.79556
[200]   validation_0-rmse:5750.85887    validation_1-rmse:5616.16472
[300]   validation_0-rmse:3949.92308    validation_1-rmse:4154.55799
[400]   validation_0-rmse:3538.33857    validation_1-rmse:3996.70155
[447]   validation_0-rmse:3472.46884    validation_1-rmse:4004.66554

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.01, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=3, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=1000, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...)

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Feature Importances

fi = pd.DataFrame(data=model.feature_importances_,
                  index=model.feature_names_in_,
                  columns=['Importance'])

fi.sort_values('Importance').plot(kind='barh',title='Feature Importance',color='blue',legend=False)

<Axes: title={'center': 'Feature Importance'}>

Predictions

train

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear	lag1	lag2	lag3
Datetime
2002-01-01 01:00:00	30393.0	1	1	1	1	2002	1	NaN	NaN	NaN
2002-01-01 02:00:00	29265.0	2	1	1	1	2002	1	NaN	NaN	NaN
2002-01-01 03:00:00	28357.0	3	1	1	1	2002	1	NaN	NaN	NaN
2002-01-01 04:00:00	27899.0	4	1	1	1	2002	1	NaN	NaN	NaN
2002-01-01 05:00:00	28057.0	5	1	1	1	2002	1	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
2017-08-01 20:00:00	45090.0	20	1	3	8	2017	213	41056.0	46225.0	43934.0
2017-08-01 21:00:00	43843.0	21	1	3	8	2017	213	40151.0	44510.0	42848.0
2017-08-01 22:00:00	41850.0	22	1	3	8	2017	213	38662.0	42467.0	40861.0
2017-08-01 23:00:00	38473.0	23	1	3	8	2017	213	35583.0	38646.0	37361.0
2017-08-02 00:00:00	35126.0	0	2	3	8	2017	214	32181.0	34829.0	33743.0

136567 rows × 10 columns

test

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear	lag1	lag2	lag3
Datetime
2017-08-03 01:00:00	29189.0	1	3	3	8	2017	215	28809.0	29952.0	28465.0
2017-08-03 02:00:00	27584.0	2	3	3	8	2017	215	27039.0	27934.0	26712.0
2017-08-03 03:00:00	26544.0	3	3	3	8	2017	215	25881.0	26659.0	25547.0
2017-08-03 04:00:00	26012.0	4	3	3	8	2017	215	25300.0	25846.0	24825.0
2017-08-03 05:00:00	26187.0	5	3	3	8	2017	215	25412.0	25898.0	24927.0
...	...	...	...	...	...	...	...	...	...	...
2018-08-02 20:00:00	44057.0	20	3	3	8	2018	214	42256.0	41485.0	38804.0
2018-08-02 21:00:00	43256.0	21	3	3	8	2018	214	41210.0	40249.0	38748.0
2018-08-02 22:00:00	41552.0	22	3	3	8	2018	214	39525.0	38698.0	37330.0
2018-08-02 23:00:00	38500.0	23	3	3	8	2018	214	36490.0	35406.0	34552.0
2018-08-03 00:00:00	35486.0	0	4	3	8	2018	215	33539.0	32094.0	31695.0

8760 rows × 10 columns

test['prediction'] = model.predict(X_test)
df = df.merge(test[['prediction']],how='left',left_index=True,right_index=True)

test['prediction']

Datetime
2017-08-03 01:00:00    27884.035156
2017-08-03 02:00:00    27147.710938
2017-08-03 03:00:00    26344.050781
2017-08-03 04:00:00    25737.550781
2017-08-03 05:00:00    25737.550781
                           ...     
2018-08-02 20:00:00    40988.347656
2018-08-02 21:00:00    40045.542969
2018-08-02 22:00:00    38405.371094
2018-08-02 23:00:00    36211.242188
2018-08-03 00:00:00    30370.074219
Name: prediction, Length: 8760, dtype: float32

test['prediction'].describe()

count     8760.000000
mean     30520.908203
std       5277.272949
min      21005.292969
25%      26730.913086
50%      30010.917969
75%      33361.808594
max      46170.230469
Name: prediction, dtype: float64

We can visualise the predicted energy consumption for a particular week:

start_date = '04-01-2018'
end_date = '04-08-2018'
filtered_df = df.loc[(df.index > start_date) & (df.index < end_date)]

plt.figure(figsize=(15, 5))
ax = sns.lineplot(data=filtered_df, x=filtered_df.index, y='PJME_MW', label='Truth')
sns.scatterplot(data=filtered_df, x=filtered_df.index, y='prediction', label='Prediction', marker='.',color='orange')
plt.title(f'Predicted vs. Actual Energy Consumption: {start_date} to {end_date}')

Text(0.5, 1.0, 'Predicted vs. Actual Energy Consumption: 04-01-2018 to 04-08-2018')

Forecasting the Future

FEATURES = ['dayofyear', 'hour', 'dayofweek', 'quarter', 'month', 'year',
            'lag1','lag2','lag3']
TARGET = 'PJME_MW'

X_all = df[FEATURES]
y_all = df[TARGET]

model.fit(X_all, 
          y_all,
          eval_set=[(X_all, y_all)],
          verbose=100)

[0] validation_0-rmse:32403.75596
[100]   validation_0-rmse:12426.47572
[200]   validation_0-rmse:5752.30602
[300]   validation_0-rmse:3971.89168
[400]   validation_0-rmse:3571.56955
[500]   validation_0-rmse:3455.87507
[600]   validation_0-rmse:3402.19853
[700]   validation_0-rmse:3365.64606
[800]   validation_0-rmse:3334.40208
[900]   validation_0-rmse:3308.46216
[999]   validation_0-rmse:3283.79395

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.01, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=3, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=1000, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=None, ...)

df.index.max()

Timestamp('2018-08-03 00:00:00')

Create future dates to predict PJME_MW for. We forecast one year beyond the end of the data.

future = pd.date_range('2018-08-03','2019-08-01',freq='1h')
future_df = pd.DataFrame(index=future)
future_df['isFuture'] = True
df['isFuture'] = False
df_and_future = pd.concat([df,future_df])
df_and_future = create_features(df_and_future)
df_and_future = add_lags(df_and_future)

df_and_future

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear	lag1	lag2	lag3	prediction	isFuture
2002-01-01 01:00:00	30393.0	1	1	1	1	2002	1	NaN	NaN	NaN	NaN	False
2002-01-01 02:00:00	29265.0	2	1	1	1	2002	1	NaN	NaN	NaN	NaN	False
2002-01-01 03:00:00	28357.0	3	1	1	1	2002	1	NaN	NaN	NaN	NaN	False
2002-01-01 04:00:00	27899.0	4	1	1	1	2002	1	NaN	NaN	NaN	NaN	False
2002-01-01 05:00:00	28057.0	5	1	1	1	2002	1	NaN	NaN	NaN	NaN	False
...	...	...	...	...	...	...	...	...	...	...	...	...
2019-07-31 20:00:00	NaN	20	2	3	7	2019	212	46912.0	39352.0	40243.0	NaN	True
2019-07-31 21:00:00	NaN	21	2	3	7	2019	212	45985.0	38699.0	39183.0	NaN	True
2019-07-31 22:00:00	NaN	22	2	3	7	2019	212	44094.0	37346.0	37759.0	NaN	True
2019-07-31 23:00:00	NaN	23	2	3	7	2019	212	40666.0	34555.0	34641.0	NaN	True
2019-08-01 00:00:00	NaN	0	3	3	8	2019	213	37158.0	31646.0	31470.0	NaN	True

154066 rows × 12 columns

future_w_features = df_and_future.query('isFuture').copy()

future_w_features

	PJME_MW	hour	dayofweek	quarter	month	year	dayofyear	lag1	lag2	lag3	prediction	isFuture
2018-08-03 00:00:00	NaN	0	4	3	8	2018	215	33539.0	32094.0	31695.0	NaN	True
2018-08-03 01:00:00	NaN	1	4	3	8	2018	215	30781.0	29503.0	29128.0	NaN	True
2018-08-03 02:00:00	NaN	2	4	3	8	2018	215	29015.0	27712.0	27373.0	NaN	True
2018-08-03 03:00:00	NaN	3	4	3	8	2018	215	27884.0	26535.0	26233.0	NaN	True
2018-08-03 04:00:00	NaN	4	4	3	8	2018	215	27223.0	25870.0	25643.0	NaN	True
...	...	...	...	...	...	...	...	...	...	...	...	...
2019-07-31 20:00:00	NaN	20	2	3	7	2019	212	46912.0	39352.0	40243.0	NaN	True
2019-07-31 21:00:00	NaN	21	2	3	7	2019	212	45985.0	38699.0	39183.0	NaN	True
2019-07-31 22:00:00	NaN	22	2	3	7	2019	212	44094.0	37346.0	37759.0	NaN	True
2019-07-31 23:00:00	NaN	23	2	3	7	2019	212	40666.0	34555.0	34641.0	NaN	True
2019-08-01 00:00:00	NaN	0	3	3	8	2019	213	37158.0	31646.0	31470.0	NaN	True

8713 rows × 12 columns

future_w_features['prediction'] = model.predict(future_w_features[FEATURES])

future_w_features['prediction']

2018-08-03 00:00:00    31290.783203
2018-08-03 01:00:00    29021.386719
2018-08-03 02:00:00    28264.789062
2018-08-03 03:00:00    27622.343750
2018-08-03 04:00:00    27305.328125
                           ...     
2019-07-31 20:00:00    42632.207031
2019-07-31 21:00:00    41855.734375
2019-07-31 22:00:00    40528.839844
2019-07-31 23:00:00    37727.152344
2019-08-01 00:00:00    32205.539062
Name: prediction, Length: 8713, dtype: float32

future_w_features['prediction'].describe()

count     8713.000000
mean     30962.250000
std       5223.142090
min      20941.083984
25%      27205.164062
50%      30601.240234
75%      33895.183594
max      46665.578125
Name: prediction, dtype: float64

plt.figure(figsize=(15,8))
sns.lineplot(data=future_w_features['prediction'],
             color=color_pal[5],
             ms=1,
             lw=1)
plt.grid(True)
plt.xlabel('Time')
plt.ylabel('Predicted Energy Consumption (MW)')
plt.title('PJME Energy Consumption, Forecasted with XGBoost')

Text(0.5, 1.0, 'PJME Energy Consumption, Forecasted with XGBoost')